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Development of an acoustic classification system for predicting rock structural stability

Brink, Stefan (2015-03)

Thesis (MSc)--Stellenbosch University, 2015.

Thesis

ENGLISH ABSTRACT: Rock falls are the cause of the majority of mining-related injuries and fatalities in
deep tabular South African mines. The standard process of entry examination is
performed before working shifts and after blasting to detect structurally loose rocks.
This process is performed by a miner using a pinch bar to ‘sound’ a rock by striking
it and making a judgement based on the frequency response of the resultant sound.
The Electronic Sounding Device (ESD) developed by the CSIR aims to assist in this
process by performing a concurrent prediction of the structural state of the rock
based on the acoustic waveform generated in the sounding process. This project
aimed to identify, develop and deploy an effective classification model to be used on
the ESD to perform this assessment.
The project was undertaken in three main stages: the collection of labelled acoustic
samples from working areas; the extraction of descriptive features from the waveforms;
and the competitive evaluation of suitable classification models.
Acoustic samples of the sounding process were recorded at the Driefontein mine
operation by teams of Gold Fields employees. The samples were recorded in working
areas on each of the four reefs that were covered by the shafts of the mine complex.
Samples were labelled as ‘safe’ or ‘unsafe’ to indicate an expert’s judgement of the
rock’s structural state. A laboratory-controlled environment was also created to
provide a platform from which to collect acoustic samples with objective labelling.
Three sets of features were extracted from the acoustic waveforms to form a
descriptive feature dataset: four statistical moments of the frequency distribution of
the waveform formed; the average energy contained in 16 discrete frequency bands
in the data; and 12 Mel Frequency Cepstral Coefficients (MFCCs).
Classification models from four model families were competitively evaluated for
best accuracy in predicting structural states. The models evaluated were k-nearest
neighbours, self-organising maps, decision trees, random forests, logistic regression,
neural networks, and support vector machines with radial basis function and polynomial
kernels. The sensitivity of the models, i.e. their ability to avoid predicting a
‘safe’ status when the rock mass was actually loose, was used as the critical performance
measure.
A single-hidden-layer feed-forward neural network with 15 nodes in the hidden
layer and a sigmoid activation function was found to best suited for acoustic classification
on the ESD. Additional feature selection was performed to identify the
optimised form of the model. The final model was successfully implemented on the
ESD platform.